Solution of trim-loss problem by an integrated simulated annealing and ordinal optimization approach

This work presents a novel optimization method capable of integrating ordinal optimization (OO) and simulated annealing (SA). A general regression neural network (GRNN) is trained using available data to generate a “rough” model that approximates the response surface in the feasible domain. A set of “good enough” candidates are generated by conducting a (SA) search on this “rough model”. Only candidates accepted by the SA search are actually tested by evaluating their true objective functions. The GRNN model is then updated using these new data. The procedure is repeated until a specified number of tests have been performed. The method (SAOO+GRNN) is tested the well-known paper trim loss problem. SAOO+GRNN approach can substantially reduce the number of function calls and the computing time far below those of simple ordinal optimization method with such as horse race selection rule, as well as straightforward simulated annealing.